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of code acceleration (GPU) Participate in numerical modelling (HPC (GPU), MPI Fortran / C, C++ Kokkos, Python, Perl) of SAMS front end and physics/test modules. Write research reports, progress reports
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patient records exploiting HPC, including GPUs embedded within NHS infrastructure. Development and deployment of ML operations software and tooling for ML / LLM algorithms working over free-text clinical
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with high-performance computing capabilities (including approximately 4,000 Nvidia RTX 4000 Ada GPUs and over 30,000 CPU cores) hosted at the project data center in Nevada where the telescope is located
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learning frameworks such as TensorFlow, or PyTorc. Experience with GPU programming and optimization for model training and inference. Familiarity with data preprocessing, feature engineering, and model
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GPUs). Research Associate: Hold a PhD in high performance computing, computational fluid dynamics or a closely related discipline*, or equivalent research, industrial or commercial experience. Research
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for early detection, differential diagnosis, progression monitoring, and treatment design. Key attractions are access to a high-performance computing cluster, NUS HPC (H100/H200 GPU clusters), two 3T Prisma
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has embraced the “infrastructure as code” approach to systems automation. You’ll be working across a range of predominately Linux based systems, including HPC and GPU accelerated compute, large-scale
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models including scaling models across a large set of GPUs; building or optimizing LLMs to tackle new, complex tasks; developing new models of brain circuits and function; and learning software engineering
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implemented in the Fortran programming language, and it relies on the platform CUDA for parallelization of the computation over several GPUs’ cores, and has interfaces with Matlab and Python for ease of use
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or more GPUs; ability to work with pre-existing codebases and get a training run going Research interest in one or more of the following: Applied ML, Natural Language Processing, Computer Vision